We select the KITTI odometry dataset [1]}, using LiDAR scans, which is made of urban and rural road scenes.
We train and test using all ground truth tagged sequences but sequence 01, on which ICP fails to register most of the scans due to scenes lacking features.
To compare against the state of the art in covariance estimation for ICP, we also present results on the challenging data sets of [2]}.
Since for the LiDAR odometry case this dataset is rather small, we refine our KITTI trained model with a few epochs of training, approximately 30 or less for early convergence, on all but one outdoor sequences and validate on the one left out.
As expected, features learned on KITTI are ineffective to predict covariances on the apartment and stairs sequences, which are highly structured small indoor environments.
Our system learns to model ICP errors with numerically small epistemic uncertainty.
For reasons of space, we only show results containing the combined aleatoric+epistemic covariance when moving to unseen datasets, i.e applying models learned on KITTI to the ETH dataset.
